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### “Continuous All k Nearest Neighbor Queries in Smartphone Networks”

Presentation Outline

### “Continuous All k Nearest Neighbor Queries in Smartphone Networks”

Demetrios Zeinalipour-Yazti

Marios D. Dikaiakos

Wang-Chien Lee

Tuesday, July 24, 2012

13th IEEE Int. Conference on Mobile Data Management (MDM’12), Bangalore, India

Smartphones

- Smartphone: A powerful sensing device!
- Processing: 1.4 GHzquad core (Samsung Exynos)
- RAM & Flash Storage:2GB & 48GB, respectively
- Networking: WiFi, 3G (Mbps) / 4G (100Mbps–1Gbps)
- Sensing: Proximity, Ambient Light, Accelerometer, Microphone, Geographic Coordinates based on AGPS (fine), WiFi or Cellular Towers (coarse).

- In-House Applications!

Airplace

(MobiSys’12, MDM’12)

SmartTrace

(ICDE’09,MDM’09,TKDE’12)

SmartP2P

(MDM’11, MDM’12)

Motivation

- Crowdsourcing with Smartphones
- A smartphone crowd is constantly moving and sensing providing large amounts of opportunistic data that enables new services and applications

"Crowdsourcing with Smartphones", Georgios Chatzimiloudis, Andreas Konstantinides, Christos Laoudias, Demetrios Zeinalipour-Yazti, IEEE Internet Computing, Special Issue: Crowdsourcing (Sep/Oct 2012), accepted May 2012. IEEE Press, 2012

Continuous k Nearest Neighbor Queries

Find 2 Closest Neighbors for 1 User

Motivation

“Create a Framework for Efficient Proximity Interactions”

Screenshots from a prototype system we've implemented for Windows Phone

http://www.zegathem.com/

Continuous All k Nearest Neighbor Queries

Find 2 Closest Neighbors for ALL User

Presentation Outline

- Motivation
- System Model and Problem Formulation
- Proximity Algorithm
- Experimental Evaluation
- Future Work

System Model

- A set of users moving in the plane of a region (u1, u2, … un)
- Area covered by a set of Network Connectivity Points (NCP)
- Each NCP creates the notion of a cell
- W.l.o.g., let the cell be represented by a circular area with an arbitrary radius

- A mobile user u is serviced at any given time point by one NCP.
- There is some centralized service, denoted as QP (Query Processor), which is aware of the coverage of each NCP.
- Each user u reports its positional information to QP regularly

Query Processor

QP

u3.

u2.

.u4

u0.

C

.u1

u6.

.

u7

u5.

Problem Definition

- Definition of the CAkNN problem:
Given a set U of nusers and their location reports ri,t ∈ R at timestep t ∈ T , then the CAkNN problem is to find in each timestep t ∈ T and for each user ui ∈ U the k objects Usol ⊆ U − uisuch that for all other objects uo ∈U − Usol − ui, dist(uk, ui) ≤ dist(uo, ui) holds

u3.

Query

Processor

u2.

.u4

u0.

C

.u1

u6.

.

u7

u5.

Naïve Solution / Related Work

Find 2-NN for u0at timestep t.

For u5?

Query Processor

Look inside your cell!

u3.

WRONG!

(u1 closer)

u2.

.u4

u0.

C

.u1

TOO EXPENSIVE!

u6.

.

u7

u5.

Perform iterative deepening!

Related Work

- Existing algorithms for CkNN (not CAkNN)
Yu et al. (YPK) 11 and Mouratidis et al. (CPM) 12

- Stateless Version: Iteratively expand the search space for each user into neighboring cells to find the kNN (like previous slide)
- Stateful Version: Improve the stateless version by utilizing previous state, under the following assumptions:

Not Appropriate for CAkNN

- i) Static Querying User (i.e., designated for point queries)
- ii) Target users move slowly (i.e., state does not decay)
- iii) Few Target users

11. Yu, Pu, Koudas. “Monitoring k-nearest neighbor queries over moving objects,” ICDE ’05

12. Mouratidis, Papadias, Hadjieleftheriou, “Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring,” SIGMOD ’05.

Presentation Outline

- Motivation
- System Model and Problem Formulation
- Proximity Algorithm
- Experimental Evaluation
- Future Work

Proximity Overview

- The first specialized algorithm for Continuous All k-NN (CAkNN) queries
- Important characteristics:
- Stateless (i.e., optimized for high mobility)
- Batch processing (i.e., with search space searching)
- Parameter-free (i.e., no tuning parameters)

- Generic operator for proximity-based queries
- See Crowdcast application presented later.

Proximity Outline For every user scan the k+-heap of his cell to find his k-NN

- For every timestep:
- Initialize a k+-heap for every cell
- Insert every user’s location report to every k+-heap
- Notice that k+-heap is a heap-based structure and most location reports will be dropped as a result of an insert operation

u3.

Query

Processor

u2.

.u4

u0.

C

.u1

u6.

.

u7

u5.

Intuition behind Proximity

- Users in a cell will share the samesearch space(search space sharing)
- Compute 1 search space per cell only!

Query

Processor

u3.

Cell

u2.

.u4

u0.

Search Space

(contains the right answers for all users in C)

C

.u1

u6.

.

u7

u5.

Proximity k+-heap

- The k+-heap structure for a cell

k+-heap structure

U

list

K

top-k heap

B

ordered list

All users inside the cell

K nearest users outside the cell

Beyondk nearest outside users (correctness)

k+-heap Construction (for Cell C)

k+-heap structure

Assume k=2.

U

list

K

top-k heap

B

ordered list

u3.

u2.

.u4

u0.

C

.u1

u6.

.

u7

u5.

k+-heap Construction (for Cell C)

k+-heap structure

Assume k=2.

U

list

K

top-k heap

B

ordered list

u3.

u2.

.u4

u0.

Diameter + dk

C

.u1

u6.

.

u7

u5.

x.

2 nearest users to cell

Presentation Outline

- Motivation
- System Model and Problem Formulation
- Proximity Algorithm
- Experimental Evaluation
- Future Work

Experimental Methodology

- Dataset:
- Oldenburg Dataset:
- Spatiotemporal generator with roadmap of Oldenburg as input (25km x 25km)
- 5000 maximum users (Oldenburg population: 160.000)

- Manhattan Dataset:
- Vehicular mobility generator with roadmap of Manhattan as input (3km x 3km)
- 500 users

- Oldenburg Dataset:
- Network connectivity points (NCPs) uniformly in space
- Communication ranges 1km, 4km and 16km for the Oldenburg dataset and ranges 1km and 4km for the Manhattan dataset

- Query: CAkNN
- Comparison: Proximity vs YPK vs CPM (using the optimal parameter value for YPK and CPM)

Proximity - Experiments

- Bottleneck for Proximity: build time
- Bottleneck for the adapted YPK and CPM: search time

Proximity - Experiments

- Benefit of Proximity in the search time
- Proximity is scalable with respect to k
- Search space for Proximity is not proportional to k like YPK, CPM

Proximity - Experiments

- Proximity: build time drops as k increases!
- Machine’s memory scheduler makes more efficient use of buffers when the search spaces are larger

Proximity - Experiments

- Proximity scales with the number of users
- Proximity outperforms YPK, CPM by an order of magnitude
- Proximity does not converge to YPK, CPM for higher values of Nmax.

The Crowdcast Application Suite

Crowdcast Suite

…

PROXIMITY

WIN7 API

Ranked 3rd in Microsoft’s ImagineCup contest local competition (to appear in the next demo session!)

Crowdcast: MsgCast

MsgCast

- Location-based Chat Channel (Post / Follow)
- - Provide Guidelines

"Get your location-based questions answered!"

Crowdcast: EyeCast

EyeCast

- Disaster Recovery Operations
- Indoor Video Conferencing Network

"Extend your Vision beyond your 2 eyes!"

- Motivation
- System Model and Problem Formulation
- Proximity Algorithm
- Experimental Evaluation
- Future Work

Proximity – Future Work

- Privacy extensions (e.g., spatial cloaking, strong privacy)
- Parallelizing server computation
- Proximity for cloud server
- User-defined k values

Future Work: SmartLab

SmartLab.cs.ucy.ac.cy

Install APK,

Upload File,

Reboot, Screenshots, Monkey Runners, etc.…

Programming cloud for the development of smartphone network applications & protocols as well as experimentation with real smartphone devices.

"Demo: A Programming Cloud of Smartphones", A. Konstantinidis, C. Costa, G. Larkou and D. Zeinalipour-Yazti, "Demo at the 10th International Conference on Mobile Systems Applications and Services" (Mobisys '12), Low Wood Bay Lake District UK, 2012

Future Work: SmartLab

Questions?

Georgios Chatzimilioudis

Demetrios Zeinalipour-Yazti

Marios D. Dikaiakos

Wang-Chien Lee

Tuesday, July 24, 2012

13th IEEE Int. Conference on Mobile Data Management (MDM’12), Bangalore, India